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ROI doesn’t mean “does it help.” Few question if it helps.

ROI is about how much its costs vs the value of the “help.” That’s where the present crisis is. Is the ROI there to pay for the trillions in commitments that have been bet on that ROI? That’s a clear no at this point hence the growing panic.

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> Is the ROI there to pay for the trillions in commitments that have been bet on that ROI? That looks like a clear no at this point.

That's only a potential crisis for those who have made concrete investments.

On the use side, the 'cost' of AI spans more than two orders of magnitude. Looking at recent models (<6mo) with reasonable performance (intelligence index >= 45) on OpenRouter, the output cost ranges from $50/MTok (Fable) to $0.153/MTok (DeepSeek Flash 0731).

From the perspective of a user of LLM/agent assistance, there's very likely a range where the benefits outweigh the cost.

If the ROI for the model developers isn't there, then that just impairs the future trajectory of the field. Current models are just bits that aren't going anywhere, and as long as they can be served (in inference) above their marginal cost they will continue to be so-delivered.

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On the value capture question, it's really not clear that the ability to write code at superhuman speed actually translates to an increase in the speed with which we can create new, better technology products that customers will pay more money for, vs. just enabling layoffs.
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You could say the same thing about compilers versus assemblers, high-level languages versus low-level ones, and services and libraries versus monolithic programs.

All other things being equal, increasing the speed of some part of the development process will increase the overall pace of development. However, By Amdahl's law that increase will be sublinear, and that is why we should take "pull requests" as an imperfect metric.

We also don't get to pick the form that 'better technology products' take. While we'd probably like to keep cost(/effort) and complexity constant and increase robustness and performance, the market equilibrium might be 'worse is better' and reward whiz-bang features and lower effort.

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Yep.

Also, large companies are still experimenting on how to integrate LLMs into their workflows. Due to the fast cadence of releases, people forget that LLMs became robust (regardless of the capability level/parameter count/data size) enough to use semi-reliably in company-specific ways only 1 year ago. And bigger the org, the slower the process. I don't expect it to settle and get productive used across the majority of very large companies for another year atleast.

High prices are mostly a result of DC capacity. As more and more DCs get built out, prices will drop. At a unit level the inference business is extremely sound regardless.

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